Welcome to the web pages of the „Scientific Machine Learning (SciML)“ group. The group was established in October 2026 under the direction of Prof. Dr. Sebastian Peitz. The mission of our team is to explore and advance methodologies for simulation, modeling, optimization and control of complex, large-scale dynamical systems.
Publications
2026
Variance representations and convergence rates for data-driven approximations of Koopman operators
F.M. Philipp, M. Schaller, S. Boshoff, S. Peitz, F. Nüske, K. Worthmann, Physica D: Nonlinear Phenomena 492 (2026).
Common pitfalls to avoid while using multiobjective optimization in machine learning
J. Akhter, P.D. Fährmann, K. Sonntag, S. Peitz, SIAM Review 68 (2026) 656–683.
Surrogate-Assisted Multi-objective Design of Complex Multibody Systems
A.C. Amakor, M.B. Berkemeier, M.C. Wohlleben, W. Sextro, S. Peitz, in: W. Senn, M. Sanguineti, A. Saudargiene, I.V. Tetko, A.E.P. Villa, V. Jirsa, Y. Bengio (Eds.), Artificial Neural Networks and Machine Learning – ICANN 2025, Springer Nature Switzerland, Cham, 2026, pp. 251–262.
SPREAD: Sampling-based Pareto front Refinement via Efficient Adaptive Diffusion
S.S. Hotegni, S. Peitz, in: 14th International Conference on Learning Representations (ICLR), 2026.
Sparse Sensor Placement in Multi-Agent Reinforcement Learning Control of Rayleigh-Bénard Convection
J. Stenner, H. Harder, S. Peitz, ArXiv:2606.30238 (2026).
Toward an Energy-Optimized Operation of Data Centers Located in Wind Farms Using Reinforcement Learning
J. Stenner, A. Kilian, S. Peitz, H. de Meer, ArXiv:2606.30316 (2026).
Enhancing Adversarial Robustness Through Multi-objective Representation Learning
S.S. Hotegni, S. Peitz, in: W. Senn, M. Sanguineti, A. Saudargiene, I.V. Tetko, A.E.P. Villa, V. Jirsa, Y. Bengio (Eds.), Artificial Neural Networks and Machine Learning – ICANN 2025, Springer Nature Switzerland, Cham, 2026, pp. 442–454.
Fourier neural operators as data-driven surrogates for two- and three-dimensional Rayleigh–Bénard convection
T. Markmann, M. Straat, S. Peitz, B. Hammer, Neurocomputing 679 (2026) 133201.
Efficient probabilistic surrogate modeling techniques for partially-observed large-scale dynamical systems
H. Harder, A. Vishwasrao, L. Guastoni, R. Vinuesa, S. Peitz, in: G. Sukhatme, L. Lindemann, S. Tu, A. Wierman, N. Atanasov (Eds.), Proceedings of The 8th Annual Learning for Dynamics and Control Conference, PMLR, 2026, pp. 1601–1619.
Interactive Pareto navigation for deep multi-task learning
A.C. Amakor, K. Sonntag, S. Peitz, in: European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), 2026, pp. 652–668.
Alle Publikationen anzeigen
Teaching
Teaching in WS 2026/27
| L.079.05728 Deep Reinforcement Learning Prof. Dr. Sebastian Peitz Mo, 12. Okt. 2026 [13:00] - Do, 4. Feb. 2027 [16:00] | Vorlesung (mit Übung) |